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59th Annual IEEE International Conference on Communications (IEEE ICC)

作者： Geng, Jiaxiang Hou, Yanzhao Tao, Xiaofeng Wang, Juncheng Luo, Bing Beijing Univ Posts & Telecommun Beijing Peoples R China Hong Kong Baptist Univ Hong Kong Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781728190549

Federated Learning (FL) algorithms commonly sample a random subset of clients to address the straggler issue and improve communication efficiency. While recent works have proposed various client sampling methods, they have limitations in joint system and data heterogeneity design, which may not align with practical heterogeneous wireless networks. In this work, we advocate a new independent client sampling strategy to minimize the wall-clock training time of FL, while considering data heterogeneity and system heterogeneity in both communication and computation. We first derive a new convergence bound for non-convex loss functions with independent client sampling and then propose an adaptive bandwidth allocation scheme. Furthermore, we propose an efficient independent client sampling algorithm based on the upper bounds on the convergence rounds and the expected per-round training time, to minimize the wall-clock time of FL, while considering both the data and system heterogeneity. Experimental results under practical wireless network settings with real-world prototype demonstrate that the proposed independent sampling scheme substantially outperforms the current best sampling schemes under various training models and datasets.

关键词： Federated learning client sampling wireless network convergence analysis optimization algorithm

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A systematic review of urban form generation and optimization for performance-driven urban design

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BUILDING AND ENVIRONMENT 2024年 253卷

作者： Zhang, Xinkai Wang, Xiaoyu Du, Sihong Tian, Shuai Jia, Ariel Ye, Yu Gao, Naiping Kuang, Xiaoming Shi, Xing Tongji Univ Coll Architecture & Urban Planning 1239 Siping Rd Shanghai 200092 Peoples R China Minist Educ Key Lab Ecol & Energy Saving Study Dense Habitat 1239 Siping Rd Shanghai 200092 Peoples R China Oakville Trafalgar High Sch 1460 Devon Rd Oakville ON L6J 3L6 Canada Tongji Univ Sch Mech Engn 4800 Caoan Rd Shanghai 201804 Peoples R China Tongji Urban Planning & Design Inst Co Ltd Res Inst Urban Design 38 Guokang Rd Shanghai 200092 Peoples R China

To address the increased energy demands and carbon emissions caused by global urbanization, it is imperative to seek high-performance urban design solutions. Urban form generation and optimization (UFGO) is a powerful way of supporting performance-driven urban design by strategically searching for a possible design space to approach optimal solutions. Relevant areas of urban form generative design, urban energy and environment simulation, and urban form optimization have been widely studied. However, UFGO, which integrates these parts into an effective workflow, is still an emerging and meaningful research field lacking a systematic review. We examined studies that utilized UFGO techniques for urban design at different scales and outlined the general workflow. An overview of the available methods and tools, as well as their basic principles for each step, namely, urban form generation, performance simulation, and optimization, is provided. The reader will be well versed in the key problems and technical paths of UFGO. According to the review, UFGO is technically feasible;nevertheless, existing limitations necessitate further exploration. Future studies should focus on developing userfriendly UFGO software packages for urban designers, systematic and flexible generative design methods, and efficient data-driven models for urban performance evaluation. In addition, an evaluation system for UFGO techniques is also required to facilitate comparative studies and the widespread application of UFGO techniques.

关键词： Urban form Generative design Performance simulation optimization algorithm

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Design of a Propeller with Global Minimum Torque

Design of a Propeller with Global Minimum Torque

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AIAA SciTech Forum

作者： Robb, Caleb Gray, Matthew N. Ferrante, Todd A. Bowers, Albion H. Oklahoma State Univ Dept Mech & Aerosp Engn Stillwater OK 74078 USA NASA Langley Res Ctr Aeronaut Syst Engn Branch 4 Langley BlvdM-S 238 Hampton VA 23681 USA Analyt Mech Associates 21 Enterprise Pkwy Hampton VA 23666 USA NASA Armstrong Flight Res Ctr Off Chief Scientist Edwards AFB CA 93523 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

Academia, industry, and Government are actively working towards a future where dozens of small to large Unmanned Aerial Systems (UAS) are present within urban environments. In this environment, high noise pollution and inefficiencies have the potential to render these new technologies impractical due to public annoyance and nonacceptance. This study aims to design and analyze a novel propeller design for minimum global torque using an optimization algorithm to find the full three dimensional solution of maximum efficiency. This resulted in a C-l distribution defining the global minimum torque solution for propeller design which corresponded with moving as much lift inboard as possible and reducing lift rapidly at the blade tip. The novel propeller (dubbed "Prandtl" propeller) was then compared with the current gold standard in propeller design, a minimum induced loss (MIL) propeller, which had all the same geometric properties except for blade twist and produced the same amount of thrust at the same advance ratio. The results of multiple iterations of Prandtl propellers showed an increase in efficiency of 2.0-3.3% for the Prandtl blade when compared to the equivalent MIL blade. A potential added benefit of this blade design is lower noise generation due to the lower lift loading at the tip reducing the large shear layer intensity which is the point source of noise in propellers. Two major breakthroughs for enabling widespread use of UAS in urban environments are noise reduction and vehicle efficiency, and this new propeller design has the potential to provide both.

关键词： Propeller Blades optimization algorithm Unmanned Aerial Systems Shear Layers Lift Coefficient Symmetric Airfoil Free Stream Velocity Lift to Drag Ratio Urban Air Mobility Airfoil Geometry

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Modeling and Detection of Cyber-Attacks in UAV Swarms using a 2D-LWR Model and Gaussian Processes

Modeling and Detection of Cyber-Attacks in UAV Swarms using ...

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AIAA SciTech Forum

作者： Kashyap, Abhishek Chakravarthy, Animesh Subbarao, Kamesh Casbeer, David Weintraub, Isaac Hencey, Brandon Univ Texas Arlington Mech & Aerosp Engn Arlington TX 76019 USA US Air Force Control Sci Ctr Res Lab Wright Patterson AFB OH 45433 USA US Air Force Aerosp Syst Directorate Res Lab Wright Patterson AFB OH 45433 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

This paper considers a class of cyber-attacks attacking a swarm of Unmanned Aerial Vehicles (UAVs). Our focus is on scenarios wherein an attacker may hack into a subset of vehicles in the swarm and create subtle changes in their parameters. These hacked vehicles (referred to as malicious vehicles) are subsequently able to modify the behavior of the overall swarm. The swarm comprising the mix of malicious and normal vehicles is modeled using a system of coupled Partial Differential Equations (PDEs) in a two-dimensional LWR model. We develop a methodology that combines Gaussian Processes (GP) with this two-species 2D PDE model, and use this method for detecting the presence of such malicious vehicles in the swarm. A Bayesian optimization scheme is employed to determine the optimal choice of basis and kernel functions that constitute the GP. Simulation results demonstrate that this detection architecture performs successful detection of the malicious vehicles, and also their mode of attack on the traffic.

关键词： Gaussian Process Unmanned Aerial Vehicle Bayesian optimization Gaussian Process Regression Basis Function Cyber Physical System optimization algorithm Smart Grid MATLAB

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Cuckoo Search algorithm with Balanced Learning to Solve Logistics Distribution Problem 18th

Cuckoo Search Algorithm with Balanced Learning to Solve Logi...

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18th International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)

作者： Li, Juan Liu, Han-xia Wuhan Business Univ Sch Informat Engn Wuhan 430058 Peoples R China Wuhan Text Univ Coll Int Business & Econ Wuhan 430202 Peoples R China

ISBN: (纸本)9789819722747;9789819722754

Cuckoo search (CS) has been successfully applied to solve various optimization problems. Despite its simplicity and efficiency, the CS is easy to suffer from the premature convergence and fall into local optimum. In this study, a differential CS extension with balanced learning namely DFCS is proposed. In DFCS, two sets including the better fitness set (FSL) and the better diversity set (DSL) are produced in the iterative process. Two excellent individuals are selected from two sets to participate, which improved the search ability by learning their beneficial behaviors in search process. The performance of DFCS algorithm is evaluated on the logistics distribution problem. The results show that DFCS algorithm has stronger competitiveness in solving logistics distribution problem than CS algorithm.

关键词： Cuckoo search algorithm Balanced-Learning optimization algorithm Diversity

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Enhancing Wine Classification Using PCA-IQPSO-SVM: A Comprehensive optimization Approach

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2024年第14期33卷 2550032-2550032页

作者： Zhu, Fenhua Hu, Qing Anhui Business Coll Sch Financial Technol Wuhu 241002 Peoples R China

In order to facilitate the high-quality advancement and digital innovation of the wine industry, a method based on principal component analysis (PCA) and improved quantum particle swarm optimization (IQPSO) to optimize support vector machine (PCA-IQPSO-SVM), was proposed to solve the wine classification problem. First, the feature extraction ability of PCA was used to reduce the input dimension of the model and improve the classification efficiency. At the same time, aiming at the problems that the quantum particle swarm optimization (QPSO) is easy to fall into local optimum and the convergence ability is decreased in the later stage of optimizing SVM, a variety of improvement strategies are used to improve QPSO to find the best parameters of SVM. The experimental results demonstrate that the model of PCA-IQPSO-SVM exhibits superior evaluation indices compared to other models. Moreover, the optimization efficiency of the PCA-IQPSO-SVM model is enhanced by 1.64% to reach an impressive 85.2%, showcasing its remarkable optimization effect. Simultaneously, this study provides a scientific approach for quality classification in the wine industry, thereby facilitating its high-quality development.

关键词： optimization algorithm principal component analysis wine classification digital economy

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An adaptive protection coordination for microgrids utilizing an improved optimization technique for user-defined DOCRs characteristics with different groups of settings considering N-1 contingency

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 248卷

作者： Merabet, O. Kheldoun, A. Bouchahdane, M. Eltom, A. Kheldoun, Ahmed Univ MHamed Bougara Inst Elect & Elect Engn Lab Signals & Syst Boumerdes Algeria Univ Tennessee Chattanooga Chattanooga TN 37403 USA Yahia Fares Univ Comp Sci Dept Medea Algeria

Due to the increased integration of distributed generation (DG) in electric power systems, optimal coordination of overcurrent relays has become a key problem in power distribution systems. However, there is a lack of expertise in the creation of optimum microgrid coordination that takes into account all N-1 scenarios through the nonstandard relay characteristics. This work presents a novel technique for optimal coordination of directional overcurrent relays (DOCRs) in terms of relay curve settings (A and B), time dial setting (TDS) and plug setting (PS) to achieve the shortest running time and attain optimal settings. The optimization is carried out using a modified version of the Hanger Games Search algorithm (MHGS). The performance of the proposed method is assessed using the 14 bus distribution system while considering all the N-1 contingencies. DIgSILENT software was utilized to perform the required power system analysis, such as power flow and short circuit analysis. The MHGS method is used to determine the best settings for the DOCRs problem. The results are compared to the traditional HGS as well as those obtained by other current optimization approaches provided in the literature in order to demonstrate the effectiveness and superiority of the proposed MHGS in lowering relay operation time for optimum DOCRs coordination.

关键词： Microgrid Optimal coordination DOCRs Protection system optimization algorithm

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Temperature Prediction of Continuous Casting Slab Based on Improved Extreme Learning Machine 153rd

Temperature Prediction of Continuous Casting Slab Based on I...

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153rd Annual Meeting & Exhibition of The-Minerals-Metals-and-Materials-Society (TMS)

作者： Jiang, Kun-chi Zhu, Ming-mei Li, Cheng-hong Zhang, Xian-Wu Lin, Hong-yu Zhang, Kai-tian Zheng, Zhong Chongqing Univ Coll Mat Sci & Engn Chongqing 400044 Peoples R China

ISBN: (纸本)9783031503511;9783031503498;9783031503481

A fusion ELM model based on ensemble learning was proposed to predict slab temperature. Combined with the actual production data of a steel mill, the integrated ELM, ELM, ANN, BP networks are designed and the comparison test proves that the integrated ELM model has more advantages in the aspects of running time and prediction accuracy. The number of base models for integrating ELM model is determined to be 3, the number of hidden layer nodes is 20, and the neuron activation function Hardlim is the most suitable for steel mill data set. The results show that the average hit rate of the predicted temperature is 88.89% within +/- 5 degrees C, and the MAE and RMSE of the predicted results are 2.46 and 2.85 degrees C, respectively, indicating that the model has high accuracy and stability, and can be used to predict the casting slab temperature.

关键词： Continuous casting slab Extreme learning machine Temperature prediction model optimization algorithm

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Neutrosophic Fuzzy Hybrid Method Using Metaheuristic algorithms for IMRT Treatment

Neutrosophic Fuzzy Hybrid Method Using Metaheuristic Algorit...

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International Conference on Intelligent and Fuzzy Systems (INFUS)

作者： Kumar, K. Keshav Narasimham, N. V. S. L. Jawaharlal Nehru Technol Univ Dept Math Hyderabad 500085 Telangana India G Narayanamma Inst Technol & Sci Dept Humanities & Math Hyderabad 500104 Telangana India

ISBN: (纸本)9783031671913;9783031671920

Intensity-Modulated Radiation Therapy (IMRT) has gained prominence in cancer treatment due to its ability to deliver precise radiation doses to tumor regions while sparing adjacent healthy tissues. Optimizing the treatment planning process in IMRT involves addressing complex, conflicting objectives and uncertainties inherent in medical decision-making. This paper proposes a novel Neutrosophic Fuzzy Hybrid Method (NFHM) integrated with metaheuristic algorithms to enhance the efficiency and robustness of IMRT treatment planning. The Neutrosophic Fuzzy Hybrid Method combines neutrosophic logic, fuzzy logic, and metaheuristic optimization techniques to model and handle uncertainties, imprecisions, and conflicting information present in the IMRT treatment planning domain. Neutrosophic logic provides a valuable framework for representing indeterminacy, while fuzzy logic aids in capturing vagueness in medical data. To optimize IMRT treatment plans, the proposed method incorporates metaheuristic algorithms such as Genetic algorithms (GA), Particle Swarm optimization (PSO), and Simulated Annealing (SA). These algorithms explore the solution space efficiently, seeking optimal trade-offs among conflicting treatment objectives, including target coverage, organ-at-risk sparing, and dose uniformity. The performance of the Neutrosophic Fuzzy Hybrid Method is evaluated using benchmark datasets and compared against traditional optimization approaches. The results demonstrate the method's superiority in generating high-quality IMRT plans that exhibit improved dose distribution and conformity while considering uncertainties and imprecisions. In conclusion, the integration of neutrosophic and fuzzy reasoning with metaheuristic optimization algorithms in the proposed NFHM presents a promising approach for enhancing the IMRT treatment planning process. The method's ability to handle uncertainties and find optimal solutions makes it a valuable tool for improving the efficacy and precision

关键词： IMRT Neutrosophic Fuzzy Hybrid Method optimization algorithm

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Dynamic Mode Decomposition For Improved Numerical Stability of Finite Volume Simulations

Dynamic Mode Decomposition For Improved Numerical Stability ...

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AIAA SciTech Forum

作者： Zandsalimy, Mohammad Ollivier-Gooch, Carl Univ British Columbia Mech Engn Adv Numer Simulat Lab Vancouver BC V6T 1Z4 Canada

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

We propose a novel approach to mesh optimization for improved stability of finite-volume simulations using dynamic mode decomposition on a subset of the solution vectors. A minimal number of the most recent solution vectors in the simulation are selected for dynamic mode decomposition. The eigenvalues of the Koopman matrix depict the magnitude growth rate and oscillation frequency of the largest solution modes. The computational cost of this method depends on the number of solution vectors in use, which is considerably less expensive compared to the eigenanalysis of the full Jacobian matrix. The dynamic eigenvectors are utilized to identify which control volumes and vertices have the greatest impact on each dynamic solution mode. The gradients of the Jacobian matrix diagonal are calculated with respect to the movement of the selected vertices. The positions of these points are adjusted to increase the diagonal dominance of the Jacobian matrix on the corresponding rows. The results verify the effectiveness and feasibility of the novel approach in numerical stability improvement through unstructured mesh optimization. This state-of-the-art method addresses the challenges faced by the latest studies in this field with full automation of the mesh optimization process and substantial computational savings.

关键词： Numerical Simulation Dynamic Mode Decomposition Computational Fluid Dynamics Finite Volume Method Linear Time Invariant System optimization algorithm Computing Angle of Attack Free Stream Unsupervised Anomaly Detection

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